Harnessing Artificial Intelligence for Risk Stratification and Outcome Prediction in Urologic Cancers: A Systematic Review

Abstract

BACKGROUND AND OBJECTIVE: Digital pathology-based artificial intelligence (DP-AI) biomarkers are emerging as transformative tools to guide clinical management of patients affected by various malignancies. We aimed to synthesise current evidence regarding their prognostic and predictive utility in urologic cancers. METHODS: In this prospectively registered systematic review (PROSPERO: CRD420251036536), we searched MEDLINE, Embase, and Web of Science in April 2025 for studies evaluating the prognostic and predictive values of DP-AI models in patients with prostate (PCa), bladder (BCa), renal cell (RCC), testicular (TCa), or penile (PeCa) cancer. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool. Results were tabulated and summarised qualitatively. KEY FINDINGS AND LIMITATIONS: Of the 1537 screened individual records, we included 31 studies validating DP-AI models in 21 155 patients. Nineteen studies were conducted in PCa (n = 17 541), six in BCa (n = 2349), five in RCC (n = 1176), and one in TCa (n = 89) patients. Ten PCa studies (n = 8951) utilised the ArteraAI model, including two (n = 2786) showing that it allows identification of patients treated with radiotherapy for clinically localised PCa that can safely omit short-term (subdistribution hazard ratio [sHR] 0.34; 95% confidence interval [CI]: 0.19-0.63) or long-term (sHR 0.55; 95% CI: 0.41-0.73) androgen deprivation therapy. Two studies (n = 894) developed and validated a model allowing identification of patients with non-muscle-invasive BCa poorly responding to Bacillus Calmette-Guérin (HR 2.3; 95% CI: 1.9-2.8), including one study (n = 253) validating a predictive biomarker for patients who may benefit from upfront gemcitabine/docetaxel. Many DP-AI models showed a prognostic association in localised PCa (n = 16 863), metastatic PCa (n = 678), non-muscle-invasive BCa (n = 2069), muscle-invasive BCa (n = 280), localised RCC (n = 1176), and germline TCa (n = 89) settings. None of the included studies assessed DP-AI models prospectively. CONCLUSIONS AND CLINICAL IMPLICATIONS: DP-AI biomarkers hold promise to improve treatment personalisation through integration into clinical practice. Prospective validation is now required.

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